Medical treatment of damaged tissue (especially that of ear, nose, throat) often requires physiological measurement. Upper airway conditions such as subglottic stenosis and sleep apnea restricts breathing and may require surgical repair to widen the airway. Accordingly, the measurement of the diameter of an airway facilitates the diagnosis of the severity of the illness and the determination of the required treatment.
There are two common options for measuring the size of the pediatric airway, each less then perfect. Computed Tomography (CT) scanning can be used to provide accurate measurements. While this method provides substantially accurate results, the radiation exposure of the bodily tissue has significant associated risks, especially in pediatric patients. As an alternative, the anatomy of the airway can be measured by fitting a series of graduated endotracheal tubes into the airway during an endoscopic examination. This method is not accurate and may cause further damage to the airway.
Surgical robotics (particularly when used for minimally invasice and natural orifice translumenal endoscopic surgery) has driven the need for three-dimensional (3D) modeling of visible light images obtained from endoscopes and laparoscopes. Stereo laparoscopes exist and are used clinically. No widely used commercial stereo endoscope currently exists, in part due to the smaller diameter (4.5 mm as opposed to 5- to 10 mm for laparoscopes).
State of the art reconstruction techniques such as, for example, those discussed by Agarwal et al. (Reconstructing rome, in IEEE Computer, pp. 40-47, 2010), Goesele et al. (Scene reconstruction from community photo collections, in Computer, vol. 43, pp, 48-53, 2010), and Frahm et al. (Building rome on a cloudless day, in ECCV. Berlin, Heidelberg, pp. 368-381; Springer-Verlag, 2010) rely heavily on the ability to extract and match salient image features. While such approach may work reasonably well with images obtained under the conditions of consistent lighting of the imaged scene and the scene that has well-defined edges and corners, the biological tissue is smooth and specularly reflective (especially when wet). Therefore, features such as SIFT and RIFT become unreliable.
Similarly, while methods such as SCRAMSAC (see, for example, Sattler et al., Scramsac: Improving ransac's efficiency with a spatial consistency filter, in ICCV, pp. 2090-2097, 2009) and ASKC (see Wang et al., A generalized kernel consensus-based robust estimator, in IEEE TPAMI, vol. 32, pp. 178-184, January 2010) can be used to compute camera motion with much more than 50 percent outlier data, the small amount of remaining inlier data is not sufficient for estimating the measured surface.
Another approach (devised to determine the shape of the imaged surface with the use of fast shape from the shading method based on the estimates of local surface changes, see Wang et al., in Measurement Science and Technology, vol. 20, no. 12, p. 125801, 2009) relies on a measurement of relative surface deformation and, as a result, does not contain metric descriptors and characteristics of the imaged surface, thereby limiting the utility of the application of the approach to the recovery of the airway diameter. Moreover, this approach assumes a constant albedo. Under such an assumption, specularities and blood vessels may cause imaging artifacts in the resulting reconstruction of the surface.
There remains a need in a system and method that enable a reconstruction of an imaged surface in association with true, metric scale and facilitate absolute measurement of the geometry of the imaged specularly reflective and smooth anatomical regions.